MIMIC-IV contains deidentified electronic health records for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center between 2009 and 2019.
The dates are randomly shifted to preserve patient privacy, but the approximate year is provided to let researchers study time trends in the data.
Here we consider the benchmark task of predicting in-hospital mortality [1]} using physiological signals, such as heart rate, pulse oximetry, and arterial blood pressure.
We simulate a model developer who trains a random forest classifier and updates it on a quarterly basis by retraining on data from the past two years.
We use the hinge loss to evaluate the models and set the abstention cost to \(\delta = 0.1\) .
